Overview

Dataset statistics

Number of variables17
Number of observations150482
Missing cells363
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory117.2 MiB
Average record size in memory816.8 B

Variable types

Text6
Categorical4
Numeric7

Alerts

State is highly imbalanced (99.4%)Imbalance
Postal Code is highly skewed (γ1 = -29.86165499)Skewed
2020 Census Tract is highly skewed (γ1 = -25.96538084)Skewed
DOL Vehicle ID has unique valuesUnique
Electric Range has 69698 (46.3%) zerosZeros
Base MSRP has 147027 (97.7%) zerosZeros

Reproduction

Analysis started2024-04-09 22:14:23.636360
Analysis finished2024-04-09 22:14:40.087642
Duration16.45 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct9529
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size9.6 MiB
2024-04-10T03:44:40.381195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1504820
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1818 ?
Unique (%)1.2%

Sample

1st rowKM8K33AGXL
2nd row1C4RJYB61N
3rd row1C4RJYD61P
4th row5YJ3E1EA7J
5th rowWBY7Z8C5XJ
ValueCountFrequency (%)
7saygdee7p 807
 
0.5%
7saygdee6p 802
 
0.5%
7saygdee8p 775
 
0.5%
7saygdee2p 775
 
0.5%
7saygdeexp 772
 
0.5%
7saygdee0p 763
 
0.5%
7saygdee5p 762
 
0.5%
7saygdee9p 756
 
0.5%
7saygdee1p 744
 
0.5%
7saygdee4p 744
 
0.5%
Other values (9519) 142782
94.9%
2024-04-10T03:44:40.887480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 137425
 
9.1%
1 111986
 
7.4%
A 91711
 
6.1%
Y 87803
 
5.8%
J 81174
 
5.4%
5 76074
 
5.1%
P 66207
 
4.4%
3 63505
 
4.2%
G 57119
 
3.8%
D 56484
 
3.8%
Other values (23) 675332
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1504820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 137425
 
9.1%
1 111986
 
7.4%
A 91711
 
6.1%
Y 87803
 
5.8%
J 81174
 
5.4%
5 76074
 
5.1%
P 66207
 
4.4%
3 63505
 
4.2%
G 57119
 
3.8%
D 56484
 
3.8%
Other values (23) 675332
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1504820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 137425
 
9.1%
1 111986
 
7.4%
A 91711
 
6.1%
Y 87803
 
5.8%
J 81174
 
5.4%
5 76074
 
5.1%
P 66207
 
4.4%
3 63505
 
4.2%
G 57119
 
3.8%
D 56484
 
3.8%
Other values (23) 675332
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1504820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 137425
 
9.1%
1 111986
 
7.4%
A 91711
 
6.1%
Y 87803
 
5.8%
J 81174
 
5.4%
5 76074
 
5.1%
P 66207
 
4.4%
3 63505
 
4.2%
G 57119
 
3.8%
D 56484
 
3.8%
Other values (23) 675332
44.9%

County
Text

Distinct183
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Memory size9.0 MiB
2024-04-10T03:44:41.094464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length20
Median length4
Mean length5.4550203
Min length3

Characters and Unicode

Total characters820866
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)0.1%

Sample

1st rowKing
2nd rowKing
3rd rowYakima
4th rowKing
5th rowThurston
ValueCountFrequency (%)
king 79075
51.9%
snohomish 17307
 
11.4%
pierce 11542
 
7.6%
clark 8855
 
5.8%
thurston 5403
 
3.5%
kitsap 4923
 
3.2%
spokane 3690
 
2.4%
whatcom 3668
 
2.4%
benton 1801
 
1.2%
skagit 1658
 
1.1%
Other values (189) 14444
 
9.5%
2024-04-10T03:44:41.486115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 119854
14.6%
n 117778
14.3%
K 84750
10.3%
g 81317
9.9%
o 52903
 
6.4%
h 44899
 
5.5%
a 37541
 
4.6%
s 33176
 
4.0%
e 32590
 
4.0%
r 29525
 
3.6%
Other values (42) 186533
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 820866
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 119854
14.6%
n 117778
14.3%
K 84750
10.3%
g 81317
9.9%
o 52903
 
6.4%
h 44899
 
5.5%
a 37541
 
4.6%
s 33176
 
4.0%
e 32590
 
4.0%
r 29525
 
3.6%
Other values (42) 186533
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 820866
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 119854
14.6%
n 117778
14.3%
K 84750
10.3%
g 81317
9.9%
o 52903
 
6.4%
h 44899
 
5.5%
a 37541
 
4.6%
s 33176
 
4.0%
e 32590
 
4.0%
r 29525
 
3.6%
Other values (42) 186533
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 820866
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 119854
14.6%
n 117778
14.3%
K 84750
10.3%
g 81317
9.9%
o 52903
 
6.4%
h 44899
 
5.5%
a 37541
 
4.6%
s 33176
 
4.0%
e 32590
 
4.0%
r 29525
 
3.6%
Other values (42) 186533
22.7%

City
Text

Distinct683
Distinct (%)0.5%
Missing3
Missing (%)< 0.1%
Memory size9.4 MiB
2024-04-10T03:44:41.823487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length24
Median length22
Mean length8.2163026
Min length3

Characters and Unicode

Total characters1236381
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique221 ?
Unique (%)0.1%

Sample

1st rowSeattle
2nd rowBothell
3rd rowYakima
4th rowKirkland
5th rowOlympia
ValueCountFrequency (%)
seattle 25675
 
14.7%
bellevue 7691
 
4.4%
redmond 5502
 
3.2%
vancouver 5310
 
3.0%
bothell 4861
 
2.8%
kirkland 4622
 
2.6%
sammamish 4436
 
2.5%
island 4427
 
2.5%
renton 4043
 
2.3%
olympia 3634
 
2.1%
Other values (715) 104345
59.8%
2024-04-10T03:44:42.307532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 169201
13.7%
a 120654
 
9.8%
l 109863
 
8.9%
t 86823
 
7.0%
n 81292
 
6.6%
o 72359
 
5.9%
r 51224
 
4.1%
i 49204
 
4.0%
S 43062
 
3.5%
d 41702
 
3.4%
Other values (42) 410997
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1236381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 169201
13.7%
a 120654
 
9.8%
l 109863
 
8.9%
t 86823
 
7.0%
n 81292
 
6.6%
o 72359
 
5.9%
r 51224
 
4.1%
i 49204
 
4.0%
S 43062
 
3.5%
d 41702
 
3.4%
Other values (42) 410997
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1236381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 169201
13.7%
a 120654
 
9.8%
l 109863
 
8.9%
t 86823
 
7.0%
n 81292
 
6.6%
o 72359
 
5.9%
r 51224
 
4.1%
i 49204
 
4.0%
S 43062
 
3.5%
d 41702
 
3.4%
Other values (42) 410997
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1236381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 169201
13.7%
a 120654
 
9.8%
l 109863
 
8.9%
t 86823
 
7.0%
n 81292
 
6.6%
o 72359
 
5.9%
r 51224
 
4.1%
i 49204
 
4.0%
S 43062
 
3.5%
d 41702
 
3.4%
Other values (42) 410997
33.2%

State
Categorical

IMBALANCE 

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
WA
150141 
CA
 
92
VA
 
35
MD
 
33
TX
 
20
Other values (36)
 
161

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters300964
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 150141
99.8%
CA 92
 
0.1%
VA 35
 
< 0.1%
MD 33
 
< 0.1%
TX 20
 
< 0.1%
NC 13
 
< 0.1%
IL 12
 
< 0.1%
AZ 11
 
< 0.1%
CO 11
 
< 0.1%
FL 9
 
< 0.1%
Other values (31) 105
 
0.1%

Length

2024-04-10T03:44:42.493167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wa 150141
99.8%
ca 92
 
0.1%
va 35
 
< 0.1%
md 33
 
< 0.1%
tx 20
 
< 0.1%
nc 13
 
< 0.1%
il 12
 
< 0.1%
az 11
 
< 0.1%
co 11
 
< 0.1%
fl 9
 
< 0.1%
Other values (31) 105
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 150299
49.9%
W 150143
49.9%
C 136
 
< 0.1%
M 43
 
< 0.1%
N 42
 
< 0.1%
V 41
 
< 0.1%
D 41
 
< 0.1%
T 30
 
< 0.1%
L 27
 
< 0.1%
O 25
 
< 0.1%
Other values (15) 137
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300964
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 150299
49.9%
W 150143
49.9%
C 136
 
< 0.1%
M 43
 
< 0.1%
N 42
 
< 0.1%
V 41
 
< 0.1%
D 41
 
< 0.1%
T 30
 
< 0.1%
L 27
 
< 0.1%
O 25
 
< 0.1%
Other values (15) 137
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300964
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 150299
49.9%
W 150143
49.9%
C 136
 
< 0.1%
M 43
 
< 0.1%
N 42
 
< 0.1%
V 41
 
< 0.1%
D 41
 
< 0.1%
T 30
 
< 0.1%
L 27
 
< 0.1%
O 25
 
< 0.1%
Other values (15) 137
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300964
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 150299
49.9%
W 150143
49.9%
C 136
 
< 0.1%
M 43
 
< 0.1%
N 42
 
< 0.1%
V 41
 
< 0.1%
D 41
 
< 0.1%
T 30
 
< 0.1%
L 27
 
< 0.1%
O 25
 
< 0.1%
Other values (15) 137
 
< 0.1%

Postal Code
Real number (ℝ)

SKEWED 

Distinct823
Distinct (%)0.5%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean98168.344
Minimum1730
Maximum99577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-04-10T03:44:42.670705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile98006
Q198052
median98122
Q398370
95-th percentile98926
Maximum99577
Range97847
Interquartile range (IQR)318

Descriptive statistics

Standard deviation2473.6122
Coefficient of variation (CV)0.025197656
Kurtosis937.83007
Mean98168.344
Median Absolute Deviation (MAD)99
Skewness-29.861655
Sum1.4772274 × 1010
Variance6118757.2
MonotonicityNot monotonic
2024-04-10T03:44:42.877182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 3869
 
2.6%
98012 2753
 
1.8%
98033 2619
 
1.7%
98006 2457
 
1.6%
98004 2456
 
1.6%
98115 2359
 
1.6%
98074 2141
 
1.4%
98072 2095
 
1.4%
98040 2083
 
1.4%
98188 2074
 
1.4%
Other values (813) 125573
83.4%
ValueCountFrequency (%)
1730 1
< 0.1%
1731 1
< 0.1%
1824 1
< 0.1%
3804 1
< 0.1%
6355 1
< 0.1%
6371 1
< 0.1%
6379 2
< 0.1%
6385 1
< 0.1%
6443 2
< 0.1%
7003 1
< 0.1%
ValueCountFrequency (%)
99577 1
 
< 0.1%
99403 53
 
< 0.1%
99402 10
 
< 0.1%
99371 1
 
< 0.1%
99362 288
0.2%
99361 10
 
< 0.1%
99360 4
 
< 0.1%
99357 17
 
< 0.1%
99356 1
 
< 0.1%
99354 250
0.2%

Model Year
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.0054
Minimum1997
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-04-10T03:44:43.047840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2014
Q12018
median2021
Q32023
95-th percentile2023
Maximum2024
Range27
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0152087
Coefficient of variation (CV)0.0014926736
Kurtosis0.23541985
Mean2020.0054
Median Absolute Deviation (MAD)2
Skewness-0.97824543
Sum3.0397446 × 108
Variance9.0914837
MonotonicityNot monotonic
2024-04-10T03:44:43.321086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2023 37079
24.6%
2022 27799
18.5%
2021 18684
12.4%
2018 14441
 
9.6%
2020 11294
 
7.5%
2019 10717
 
7.1%
2017 8574
 
5.7%
2016 5650
 
3.8%
2015 4934
 
3.3%
2013 4566
 
3.0%
Other values (12) 6744
 
4.5%
ValueCountFrequency (%)
1997 1
 
< 0.1%
1998 1
 
< 0.1%
1999 4
 
< 0.1%
2000 8
 
< 0.1%
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2008 19
 
< 0.1%
2010 24
 
< 0.1%
2011 796
0.5%
2012 1633
1.1%
ValueCountFrequency (%)
2024 642
 
0.4%
2023 37079
24.6%
2022 27799
18.5%
2021 18684
12.4%
2020 11294
 
7.5%
2019 10717
 
7.1%
2018 14441
 
9.6%
2017 8574
 
5.7%
2016 5650
 
3.8%
2015 4934
 
3.3%

Make
Categorical

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 MiB
TESLA
68983 
NISSAN
13497 
CHEVROLET
12026 
FORD
7614 
BMW
 
6439
Other values (32)
41923 

Length

Max length20
Median length14
Mean length5.5494345
Min length3

Characters and Unicode

Total characters835090
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHYUNDAI
2nd rowJEEP
3rd rowJEEP
4th rowTESLA
5th rowBMW

Common Values

ValueCountFrequency (%)
TESLA 68983
45.8%
NISSAN 13497
 
9.0%
CHEVROLET 12026
 
8.0%
FORD 7614
 
5.1%
BMW 6439
 
4.3%
KIA 6198
 
4.1%
TOYOTA 5223
 
3.5%
VOLKSWAGEN 4074
 
2.7%
VOLVO 3536
 
2.3%
JEEP 3292
 
2.2%
Other values (27) 19600
 
13.0%

Length

2024-04-10T03:44:43.515183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 68983
45.8%
nissan 13497
 
9.0%
chevrolet 12026
 
8.0%
ford 7614
 
5.1%
bmw 6439
 
4.3%
kia 6198
 
4.1%
toyota 5223
 
3.5%
volkswagen 4074
 
2.7%
volvo 3536
 
2.3%
jeep 3292
 
2.2%
Other values (32) 19674
 
13.1%

Most occurring characters

ValueCountFrequency (%)
E 112796
13.5%
A 111084
13.3%
S 108515
13.0%
T 94158
11.3%
L 93227
11.2%
O 44137
 
5.3%
N 40005
 
4.8%
I 36504
 
4.4%
R 31516
 
3.8%
V 25702
 
3.1%
Other values (18) 137446
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 835090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 112796
13.5%
A 111084
13.3%
S 108515
13.0%
T 94158
11.3%
L 93227
11.2%
O 44137
 
5.3%
N 40005
 
4.8%
I 36504
 
4.4%
R 31516
 
3.8%
V 25702
 
3.1%
Other values (18) 137446
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 835090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 112796
13.5%
A 111084
13.3%
S 108515
13.0%
T 94158
11.3%
L 93227
11.2%
O 44137
 
5.3%
N 40005
 
4.8%
I 36504
 
4.4%
R 31516
 
3.8%
V 25702
 
3.1%
Other values (18) 137446
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 835090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 112796
13.5%
A 111084
13.3%
S 108515
13.0%
T 94158
11.3%
L 93227
11.2%
O 44137
 
5.3%
N 40005
 
4.8%
I 36504
 
4.4%
R 31516
 
3.8%
V 25702
 
3.1%
Other values (18) 137446
16.5%

Model
Text

Distinct127
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.1 MiB
2024-04-10T03:44:43.818166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length24
Median length7
Mean length6.3729017
Min length2

Characters and Unicode

Total characters959007
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowKONA
2nd rowGRAND CHEROKEE
3rd rowGRAND CHEROKEE
4th rowMODEL 3
5th rowI3
ValueCountFrequency (%)
model 68936
28.8%
y 28502
11.9%
3 27709
11.6%
leaf 13187
 
5.5%
s 7611
 
3.2%
bolt 6886
 
2.9%
ev 5962
 
2.5%
x 5114
 
2.1%
volt 4890
 
2.0%
prime 4052
 
1.7%
Other values (124) 66507
27.8%
2024-04-10T03:44:44.279090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 113869
11.9%
L 103596
 
10.8%
O 97230
 
10.1%
88874
 
9.3%
M 80285
 
8.4%
D 74731
 
7.8%
A 41128
 
4.3%
I 31988
 
3.3%
3 31749
 
3.3%
Y 30819
 
3.2%
Other values (28) 264738
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 959007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 113869
11.9%
L 103596
 
10.8%
O 97230
 
10.1%
88874
 
9.3%
M 80285
 
8.4%
D 74731
 
7.8%
A 41128
 
4.3%
I 31988
 
3.3%
3 31749
 
3.3%
Y 30819
 
3.2%
Other values (28) 264738
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 959007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 113869
11.9%
L 103596
 
10.8%
O 97230
 
10.1%
88874
 
9.3%
M 80285
 
8.4%
D 74731
 
7.8%
A 41128
 
4.3%
I 31988
 
3.3%
3 31749
 
3.3%
Y 30819
 
3.2%
Other values (28) 264738
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 959007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 113869
11.9%
L 103596
 
10.8%
O 97230
 
10.1%
88874
 
9.3%
M 80285
 
8.4%
D 74731
 
7.8%
A 41128
 
4.3%
I 31988
 
3.3%
3 31749
 
3.3%
Y 30819
 
3.2%
Other values (28) 264738
27.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
Battery Electric Vehicle (BEV)
116807 
Plug-in Hybrid Electric Vehicle (PHEV)
33675 

Length

Max length38
Median length30
Mean length31.790247
Min length30

Characters and Unicode

Total characters4783860
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBattery Electric Vehicle (BEV)
2nd rowPlug-in Hybrid Electric Vehicle (PHEV)
3rd rowPlug-in Hybrid Electric Vehicle (PHEV)
4th rowBattery Electric Vehicle (BEV)
5th rowPlug-in Hybrid Electric Vehicle (PHEV)

Common Values

ValueCountFrequency (%)
Battery Electric Vehicle (BEV) 116807
77.6%
Plug-in Hybrid Electric Vehicle (PHEV) 33675
 
22.4%

Length

2024-04-10T03:44:44.483660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T03:44:44.689310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
electric 150482
23.7%
vehicle 150482
23.7%
battery 116807
18.4%
bev 116807
18.4%
plug-in 33675
 
5.3%
hybrid 33675
 
5.3%
phev 33675
 
5.3%

Most occurring characters

ValueCountFrequency (%)
e 568253
11.9%
485121
10.1%
c 451446
9.4%
t 384096
 
8.0%
i 368314
 
7.7%
l 334639
 
7.0%
V 300964
 
6.3%
r 300964
 
6.3%
E 300964
 
6.3%
B 233614
 
4.9%
Other values (13) 1055485
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4783860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 568253
11.9%
485121
10.1%
c 451446
9.4%
t 384096
 
8.0%
i 368314
 
7.7%
l 334639
 
7.0%
V 300964
 
6.3%
r 300964
 
6.3%
E 300964
 
6.3%
B 233614
 
4.9%
Other values (13) 1055485
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4783860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 568253
11.9%
485121
10.1%
c 451446
9.4%
t 384096
 
8.0%
i 368314
 
7.7%
l 334639
 
7.0%
V 300964
 
6.3%
r 300964
 
6.3%
E 300964
 
6.3%
B 233614
 
4.9%
Other values (13) 1055485
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4783860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 568253
11.9%
485121
10.1%
c 451446
9.4%
t 384096
 
8.0%
i 368314
 
7.7%
l 334639
 
7.0%
V 300964
 
6.3%
r 300964
 
6.3%
E 300964
 
6.3%
B 233614
 
4.9%
Other values (13) 1055485
22.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.1 MiB
Eligibility unknown as battery range has not been researched
69698 
Clean Alternative Fuel Vehicle Eligible
62951 
Not eligible due to low battery range
17833 

Length

Max length60
Median length39
Mean length48.489454
Min length37

Characters and Unicode

Total characters7296790
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClean Alternative Fuel Vehicle Eligible
2nd rowNot eligible due to low battery range
3rd rowNot eligible due to low battery range
4th rowClean Alternative Fuel Vehicle Eligible
5th rowClean Alternative Fuel Vehicle Eligible

Common Values

ValueCountFrequency (%)
Eligibility unknown as battery range has not been researched 69698
46.3%
Clean Alternative Fuel Vehicle Eligible 62951
41.8%
Not eligible due to low battery range 17833
 
11.9%

Length

2024-04-10T03:44:44.837299image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T03:44:44.970846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
battery 87531
 
8.2%
range 87531
 
8.2%
not 87531
 
8.2%
eligible 80784
 
7.6%
eligibility 69698
 
6.5%
been 69698
 
6.5%
unknown 69698
 
6.5%
researched 69698
 
6.5%
has 69698
 
6.5%
as 69698
 
6.5%
Other values (7) 305303
28.6%

Most occurring characters

ValueCountFrequency (%)
e 1017708
13.9%
916386
12.6%
l 570601
 
7.8%
i 566262
 
7.8%
n 561923
 
7.7%
a 510058
 
7.0%
t 476026
 
6.5%
r 377409
 
5.2%
b 307711
 
4.2%
g 238013
 
3.3%
Other values (16) 1754693
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7296790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1017708
13.9%
916386
12.6%
l 570601
 
7.8%
i 566262
 
7.8%
n 561923
 
7.7%
a 510058
 
7.0%
t 476026
 
6.5%
r 377409
 
5.2%
b 307711
 
4.2%
g 238013
 
3.3%
Other values (16) 1754693
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7296790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1017708
13.9%
916386
12.6%
l 570601
 
7.8%
i 566262
 
7.8%
n 561923
 
7.7%
a 510058
 
7.0%
t 476026
 
6.5%
r 377409
 
5.2%
b 307711
 
4.2%
g 238013
 
3.3%
Other values (16) 1754693
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7296790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1017708
13.9%
916386
12.6%
l 570601
 
7.8%
i 566262
 
7.8%
n 561923
 
7.7%
a 510058
 
7.0%
t 476026
 
6.5%
r 377409
 
5.2%
b 307711
 
4.2%
g 238013
 
3.3%
Other values (16) 1754693
24.0%

Electric Range
Real number (ℝ)

ZEROS 

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.877839
Minimum0
Maximum337
Zeros69698
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-04-10T03:44:45.144075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18
Q397
95-th percentile266
Maximum337
Range337
Interquartile range (IQR)97

Descriptive statistics

Standard deviation96.230009
Coefficient of variation (CV)1.417694
Kurtosis-0.032045726
Mean67.877839
Median Absolute Deviation (MAD)18
Skewness1.2194867
Sum10214393
Variance9260.2146
MonotonicityNot monotonic
2024-04-10T03:44:45.335482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69698
46.3%
215 6490
 
4.3%
220 4125
 
2.7%
84 4023
 
2.7%
238 3611
 
2.4%
25 3580
 
2.4%
21 2765
 
1.8%
32 2759
 
1.8%
19 2539
 
1.7%
208 2535
 
1.7%
Other values (92) 48357
32.1%
ValueCountFrequency (%)
0 69698
46.3%
6 942
 
0.6%
8 35
 
< 0.1%
9 18
 
< 0.1%
10 166
 
0.1%
11 2
 
< 0.1%
12 164
 
0.1%
13 358
 
0.2%
14 1120
 
0.7%
15 91
 
0.1%
ValueCountFrequency (%)
337 77
 
0.1%
330 322
 
0.2%
322 1747
1.2%
308 513
 
0.3%
293 450
 
0.3%
291 2278
1.5%
289 648
 
0.4%
270 269
 
0.2%
266 1490
1.0%
265 134
 
0.1%

Base MSRP
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1312.6447
Minimum0
Maximum845000
Zeros147027
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-04-10T03:44:45.515240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum845000
Range845000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9231.3102
Coefficient of variation (CV)7.0326037
Kurtosis518.69189
Mean1312.6447
Median Absolute Deviation (MAD)0
Skewness11.930856
Sum1.975294 × 108
Variance85217088
MonotonicityNot monotonic
2024-04-10T03:44:45.710377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 147027
97.7%
69900 1441
 
1.0%
31950 399
 
0.3%
52900 218
 
0.1%
32250 152
 
0.1%
59900 134
 
0.1%
54950 133
 
0.1%
39995 117
 
0.1%
36900 101
 
0.1%
44100 97
 
0.1%
Other values (21) 663
 
0.4%
ValueCountFrequency (%)
0 147027
97.7%
31950 399
 
0.3%
32250 152
 
0.1%
32995 3
 
< 0.1%
33950 74
 
< 0.1%
34995 64
 
< 0.1%
36800 53
 
< 0.1%
36900 101
 
0.1%
39995 117
 
0.1%
43700 10
 
< 0.1%
ValueCountFrequency (%)
845000 1
 
< 0.1%
184400 11
< 0.1%
110950 21
< 0.1%
109000 7
 
< 0.1%
102000 17
< 0.1%
98950 19
< 0.1%
91250 5
 
< 0.1%
90700 19
< 0.1%
89100 6
 
< 0.1%
81100 19
< 0.1%

Legislative District
Real number (ℝ)

Distinct49
Distinct (%)< 0.1%
Missing341
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean29.34395
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-04-10T03:44:45.886054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q118
median33
Q343
95-th percentile48
Maximum49
Range48
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.824829
Coefficient of variation (CV)0.50520906
Kurtosis-1.0666383
Mean29.34395
Median Absolute Deviation (MAD)11
Skewness-0.48388861
Sum4405730
Variance219.77556
MonotonicityNot monotonic
2024-04-10T03:44:46.071674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
41 9969
 
6.6%
45 9171
 
6.1%
48 8419
 
5.6%
1 6510
 
4.3%
36 6494
 
4.3%
5 6348
 
4.2%
46 5955
 
4.0%
11 5837
 
3.9%
43 5789
 
3.8%
37 4498
 
3.0%
Other values (39) 81151
53.9%
ValueCountFrequency (%)
1 6510
4.3%
2 1636
 
1.1%
3 732
 
0.5%
4 1174
 
0.8%
5 6348
4.2%
6 1344
 
0.9%
7 696
 
0.5%
8 1523
 
1.0%
9 839
 
0.6%
10 2624
1.7%
ValueCountFrequency (%)
49 2035
 
1.4%
48 8419
5.6%
47 2685
 
1.8%
46 5955
4.0%
45 9171
6.1%
44 3790
 
2.5%
43 5789
3.8%
42 2078
 
1.4%
41 9969
6.6%
40 3349
 
2.2%

DOL Vehicle ID
Real number (ℝ)

UNIQUE 

Distinct150482
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1111223 × 108
Minimum4385
Maximum4.7925477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-04-10T03:44:46.250654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4385
5-th percentile1.06054 × 108
Q11.6934731 × 108
median2.1503063 × 108
Q32.399119 × 108
95-th percentile3.4907063 × 108
Maximum4.7925477 × 108
Range4.7925039 × 108
Interquartile range (IQR)70564590

Descriptive statistics

Standard deviation81963880
Coefficient of variation (CV)0.38824789
Kurtosis3.2904167
Mean2.1111223 × 108
Median Absolute Deviation (MAD)30710463
Skewness0.89719618
Sum3.1768591 × 1013
Variance6.7180776 × 1015
MonotonicityNot monotonic
2024-04-10T03:44:46.443338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
249675142 1
 
< 0.1%
228349978 1
 
< 0.1%
176509091 1
 
< 0.1%
249595734 1
 
< 0.1%
133676098 1
 
< 0.1%
209324048 1
 
< 0.1%
249759170 1
 
< 0.1%
252477270 1
 
< 0.1%
143888034 1
 
< 0.1%
218908910 1
 
< 0.1%
Other values (150472) 150472
> 99.9%
ValueCountFrequency (%)
4385 1
< 0.1%
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
23145 1
< 0.1%
24629 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
ValueCountFrequency (%)
479254772 1
< 0.1%
479114996 1
< 0.1%
478935460 1
< 0.1%
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478925163 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%
Distinct822
Distinct (%)0.5%
Missing7
Missing (%)< 0.1%
Memory size12.3 MiB
2024-04-10T03:44:46.733701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length35
Median length34
Mean length28.507772
Min length24

Characters and Unicode

Total characters4289707
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique287 ?
Unique (%)0.2%

Sample

1st rowPOINT (-122.34301 47.659185)
2nd rowPOINT (-122.20578 47.762405)
3rd rowPOINT (-120.6027202 46.5965625)
4th rowPOINT (-122.209285 47.71124)
5th rowPOINT (-122.89692 47.043535)
ValueCountFrequency (%)
point 150475
33.3%
47.67668 3869
 
0.9%
122.12302 3869
 
0.9%
122.1876761 2753
 
0.6%
47.820517 2753
 
0.6%
122.20264 2619
 
0.6%
47.6785 2619
 
0.6%
122.16937 2457
 
0.5%
47.571015 2457
 
0.5%
122.201905 2456
 
0.5%
Other values (1634) 275098
60.9%
2024-04-10T03:44:47.294888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 436578
 
10.2%
1 353239
 
8.2%
4 302329
 
7.0%
. 300950
 
7.0%
300950
 
7.0%
7 286992
 
6.7%
5 270420
 
6.3%
6 185031
 
4.3%
3 183732
 
4.3%
8 179569
 
4.2%
Other values (10) 1489917
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4289707
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 436578
 
10.2%
1 353239
 
8.2%
4 302329
 
7.0%
. 300950
 
7.0%
300950
 
7.0%
7 286992
 
6.7%
5 270420
 
6.3%
6 185031
 
4.3%
3 183732
 
4.3%
8 179569
 
4.2%
Other values (10) 1489917
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4289707
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 436578
 
10.2%
1 353239
 
8.2%
4 302329
 
7.0%
. 300950
 
7.0%
300950
 
7.0%
7 286992
 
6.7%
5 270420
 
6.3%
6 185031
 
4.3%
3 183732
 
4.3%
8 179569
 
4.2%
Other values (10) 1489917
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4289707
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 436578
 
10.2%
1 353239
 
8.2%
4 302329
 
7.0%
. 300950
 
7.0%
300950
 
7.0%
7 286992
 
6.7%
5 270420
 
6.3%
6 185031
 
4.3%
3 183732
 
4.3%
8 179569
 
4.2%
Other values (10) 1489917
34.7%
Distinct76
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Memory size14.6 MiB
2024-04-10T03:44:47.495872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length112
Median length110
Mean length44.3904
Min length10

Characters and Unicode

Total characters6679823
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
2nd rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
3rd rowPACIFICORP
4th rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
5th rowPUGET SOUND ENERGY INC
ValueCountFrequency (%)
of 142658
12.6%
133561
11.8%
wa 93105
 
8.2%
tacoma 91917
 
8.1%
sound 89796
 
8.0%
energy 89796
 
8.0%
puget 88942
 
7.9%
inc||city 55634
 
4.9%
power 32772
 
2.9%
inc 30080
 
2.7%
Other values (114) 280616
24.9%
2024-04-10T03:44:47.883474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
978398
14.6%
O 491151
 
7.4%
N 473175
 
7.1%
T 465430
 
7.0%
A 452151
 
6.8%
E 435801
 
6.5%
I 364719
 
5.5%
C 364699
 
5.5%
Y 244504
 
3.7%
U 233176
 
3.5%
Other values (26) 2176619
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6679823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
978398
14.6%
O 491151
 
7.4%
N 473175
 
7.1%
T 465430
 
7.0%
A 452151
 
6.8%
E 435801
 
6.5%
I 364719
 
5.5%
C 364699
 
5.5%
Y 244504
 
3.7%
U 233176
 
3.5%
Other values (26) 2176619
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6679823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
978398
14.6%
O 491151
 
7.4%
N 473175
 
7.1%
T 465430
 
7.0%
A 452151
 
6.8%
E 435801
 
6.5%
I 364719
 
5.5%
C 364699
 
5.5%
Y 244504
 
3.7%
U 233176
 
3.5%
Other values (26) 2176619
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6679823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
978398
14.6%
O 491151
 
7.4%
N 473175
 
7.1%
T 465430
 
7.0%
A 452151
 
6.8%
E 435801
 
6.5%
I 364719
 
5.5%
C 364699
 
5.5%
Y 244504
 
3.7%
U 233176
 
3.5%
Other values (26) 2176619
32.6%

2020 Census Tract
Real number (ℝ)

SKEWED 

Distinct2079
Distinct (%)1.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2971955 × 1010
Minimum1.0810419 × 109
Maximum5.6033 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-04-10T03:44:48.063138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.0810419 × 109
5-th percentile5.3011041 × 1010
Q15.303301 × 1010
median5.3033029 × 1010
Q35.3053073 × 1010
95-th percentile5.3067012 × 1010
Maximum5.6033 × 1010
Range5.4951958 × 1010
Interquartile range (IQR)20063009

Descriptive statistics

Standard deviation1.6388413 × 109
Coefficient of variation (CV)0.030937906
Kurtosis696.14031
Mean5.2971955 × 1010
Median Absolute Deviation (MAD)26909
Skewness-25.965381
Sum7.9711668 × 1015
Variance2.6858009 × 1018
MonotonicityNot monotonic
2024-04-10T03:44:48.242610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.30330282 × 10101758
 
1.2%
5.30330285 × 1010963
 
0.6%
5.303303232 × 1010699
 
0.5%
5.30330093 × 1010558
 
0.4%
5.30330262 × 1010531
 
0.4%
5.30670112 × 1010523
 
0.3%
5.303303232 × 1010522
 
0.3%
5.30330245 × 1010501
 
0.3%
5.303302501 × 1010483
 
0.3%
5.303303222 × 1010483
 
0.3%
Other values (2069) 143458
95.3%
ValueCountFrequency (%)
1081041901 1
< 0.1%
1097006803 1
< 0.1%
1117030352 1
< 0.1%
2020000206 1
< 0.1%
4013061064 1
< 0.1%
4013115900 1
< 0.1%
4013216901 1
< 0.1%
4013318700 1
< 0.1%
4013318800 1
< 0.1%
4013610301 1
< 0.1%
ValueCountFrequency (%)
5.60330001 × 10101
 
< 0.1%
5.60210007 × 10101
 
< 0.1%
5.307794001 × 10104
 
< 0.1%
5.307794001 × 10103
 
< 0.1%
5.307794 × 10101
 
< 0.1%
5.307794 × 10107
 
< 0.1%
5.307794 × 10108
 
< 0.1%
5.307794 × 10103
 
< 0.1%
5.30770034 × 101031
< 0.1%
5.30770032 × 101032
< 0.1%

Interactions

2024-04-10T03:44:37.276125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:30.441494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:31.792023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:32.923590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:34.025060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:35.060840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:36.206912image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:37.419712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:30.888412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:31.939583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:33.078822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:34.163318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:35.241281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:36.352416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:37.671037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:31.035985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:32.083348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:33.245409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:34.307384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:35.387108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:36.505237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:37.842600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:31.190119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:32.240974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:33.409634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:34.465328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:35.565254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:36.662455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:37.983646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:31.330367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:32.470424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:33.556272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:34.606823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:35.714883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:36.837006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:38.146208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:31.489297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:32.615416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:33.710826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:34.749442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:35.873922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:36.983609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:38.289716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:31.637469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:32.773025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:33.862245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:34.897049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:36.025989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T03:44:37.127528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-04-10T03:44:38.557827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-10T03:44:39.060412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
0KM8K33AGXLKingSeattleWA98103.02020HYUNDAIKONABattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible258043.0249675142POINT (-122.34301 47.659185)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303300e+10
11C4RJYB61NKingBothellWA98011.02022JEEPGRAND CHEROKEEPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range2501.0233928502POINT (-122.20578 47.762405)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
21C4RJYD61PYakimaYakimaWA98908.02023JEEPGRAND CHEROKEEPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range25014.0229675939POINT (-120.6027202 46.5965625)PACIFICORP5.307700e+10
35YJ3E1EA7JKingKirklandWA98034.02018TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible215045.0104714466POINT (-122.209285 47.71124)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
4WBY7Z8C5XJThurstonOlympiaWA98501.02018BMWI3Plug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible97022.0185498386POINT (-122.89692 47.043535)PUGET SOUND ENERGY INC5.306701e+10
55YJ3E1EAXLSnohomishMarysvilleWA98271.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible266038.0124595523POINT (-122.1713847 48.10433)PUGET SOUND ENERGY INC5.306194e+10
62C4RC1N77HKingKentWA98042.02017CHRYSLERPACIFICAPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible33047.01815593POINT (-122.111625 47.36078)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
75YJYGDEE3LKingWoodinvilleWA98072.02020TESLAMODEL YBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible291045.0124760555POINT (-122.151665 47.75855)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
85YJ3E1EA1JIslandCoupevilleWA98239.02018TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible215010.0125048003POINT (-122.6880708 48.2179983)PUGET SOUND ENERGY INC5.302997e+10
97SAYGDEF0PKingBellevueWA98004.02023TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0048.0240416207POINT (-122.201905 47.61385)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
1504725YJYGDEE2MKingFall CityWA98024.02021TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched005.0182528685POINT (-121.8947186 47.56345)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
150473YV4BR0CZ5NHowardEllicott CityMD21042.02022VOLVOXC90Plug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range180NaN185596036POINT (-76.83207 39.276485)NON WASHINGTON STATE ELECTRIC UTILITY2.402760e+10
1504745YJ3E1EA2NKingSeattleWA98122.02022TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0037.0183011300POINT (-122.30839 47.610365)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303301e+10
150475KM8KNDAF2PKingSeattleWA98105.02023HYUNDAIIONIQ 5Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0046.0221103081POINT (-122.319115 47.66132)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303300e+10
1504761G1FZ6S03NSnohomishBothellWA98021.02022CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched001.0218241271POINT (-122.179458 47.802589)PUGET SOUND ENERGY INC5.306105e+10
150477WBY43AW05PGrays HarborMontesanoWA98563.02023BMWI4Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0019.0251204075POINT (-123.60535 46.982215)BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF GRAYS HARBOR COUNTY5.302700e+10
1504785YJ3E1EB7PKingSeattleWA98104.02023TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0043.0241344414POINT (-122.329075 47.6018)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303301e+10
1504795YJYGDEEXMKingSeattleWA98109.02021TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0043.0180705626POINT (-122.34848 47.632405)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303301e+10
1504805UXTA6C08PSnohomishMountlake TerraceWA98043.02023BMWX5Plug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible3001.0240473950POINT (-122.30842 47.78416)PUGET SOUND ENERGY INC5.306105e+10
1504817SAYGDEF8NSkagitMount VernonWA98273.02022TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0040.0207667589POINT (-122.338975 48.41333)PUGET SOUND ENERGY INC5.305795e+10